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Rainfall is one of the primary sources of water for many countries in the world. Recently, the problem of variant rainfall rates has emerged in most countries, especially in the Middle East, due to the phenomenon of global warming. Consequently, this phenomenon affected all aspects of human life, especially the agricultural sector. To address this problem, machine learning algorithms were adopted to predict rainfall in Al-Diwaniya city in Iraq. A Recurrent Neural Network (RNN) algorithm based on Long- and Short-Term Memory (LSTM) technology was applied. This technique was implemented by calculating the weight of previous observations or time shift variables in the form of time series based on the simulating neural networks. This network is trained to reach the minimum Mean Square Error (MSE) rate by adjusting the values of the estimated weights for the chosen model structure. The finding of the study showed the prediction values for LSTM are better than the RNN algorithm according to the MSE values that are obtained.

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In this study, we investigated the impact of rainfall rate and temperature during fruit development on chilling injury (CI) of Queen pineapples (Ananas comosusL. Merr) after cold storage at 13 °C. The pineapples were induced to flower in October 2016 and May 2017. Rainfall and field temperature during fruit development were recorded. The fruit were harvested in March 2017 and October 2017 (135 d after anthesis), respectively. Visual appearance, CI score, lightness (L*) value, parameters related to membrane integrity, and the activities of enzymes related to browning of the tissue adjacent to the core were monitored in the fruit after 7 and 14 d of cold storage followed by room temperature storage (27 ± 1 °C) (RT) for 2 d. High CI incidence was associated with high rainfall rather than the field temperature (approx. 27–29 °C) during fruit development. The fruit harvested in March experienced higher rainfall during fruit development than the fruit harvested in October. The CI severity of the fruit harvested in March was greater than that of fruit harvested in October. The higher CI severity of the fruit harvested in March was accompanied with reduced membrane integrity as well as enzymatic browning compared to the fruit harvested in October. In conclusion, high rainfall during fruit development is a major factor for CI of Queen pineapple grown in tropical zones. The results of this investigation provide insight into the causes underlying the loss of fruit quality during storage and marketing.

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Raindrop size distributions collected by the DROPLET MEASUREMENT TECHNOLogies Precipitation imaging probe from 17 flights through 6 hurricanes during National Oceanic and Atmospheric Administration’s hurricane field program in 2020 are used to study reflectivity (Z) and rainfall rate (RR) (R) relationship (i.e., Z‐Rrelationship). The results show that the Z‐Rdistribution is highly scattered and the scatter increases with RR and reflectivity up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly. The range of the estimated RR from a power‐law Z‐Rrelationship can be as large as 50 mm hr−1at reflectivity of 40 dBZ. The result from random forest regression model demonstrates that including the information of mass‐weighted‐diameter (Dm) along with radar reflectivity improves the estimated RR significantly. One of the main applications of radar measurements is to estimate rainfall rate (RR) based on its power‐law relationships with radar reflectivity. Different relationships have been obtained for different weather scenarios. Hurricanes, in comparison with other severe weather systems, have a much longer life cycle and travel across much greater zonal and meridional extent. Therefore, in order to accurately estimate the RR for a specific time at a specific location in a hurricane, the power‐law relationship with constant coefficients needs to be improved. The uncertainties of the estimated RR using power‐law relationship are related to the raindrop sizes. The usage of a machine learning model (random forest regression) demonstrates that the uncertainties in the estimated rainfall are reduced significantly when both radar reflectivity and the drop sizes are taken into account. The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidlyThere are significant uncertainties associated with using a power law Z‐Rrelationship to estimate rainfall rate from radar reflectivityThe result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used The scatter in reflectivity (Z) ‐rainfall rate (R) distribution increases up to 48 dBZ or 25 mm hr−1, after which it decreases rapidly There are significant uncertainties associated with using a power law Z‐Rrelationship to estimate rainfall rate from radar reflectivity The result from a machine learning model shows that rainfall estimation is improved if drop size information and reflectivity are both used

The knowledge of 1-minute rainfall rate distribution is very important in order for the prediction of rainfall attenuation at any location. Due to the availability of 1-minute rainfall rate data is limited, conversion of rainfall rate distribution over a long integration time into the distribution for 1-minute is needed. Therefore several rainfall rate conversion methods were proposed.

This paper deals with a new algorithm applicable to rainfall rate profiling with a rain-attenuating-frequency radar for vertical incidence from an air or space platform. The proposed algorithm is independent from external radar calibration which is especially difficult to perform in airborne or space-borne applications. In addition to backscattering data from raindrops, path-integrated rainfall rate is used to estimate a rainfall rate profile. The path-integrated rainfall rate may be estimated from surface echo attenuation or from brightness temperature measured with an auxiliary microwave radiometer. The performance of the proposed algorithm is evaluated by a computer simulation experiment assuming backscattering and attenuation models of the radar wave due to rain. Then, airborne radar data are analysed with the algorithm to demonstrate its validity.

Knowledge about the dynamic characteristics of rain attenuation is of utmost importance for many applications in terrestrial and satellite communication systems operating at frequencies above 10 GHz. Long‐term rain rate statistics and rain rate duration statistics are usually available from meteorological data. In this paper, a spatial–temporal analysis is employed in order to evaluate the rain attenuation power spectrum of a terrestrial/satellite path. The predicted power spectrum is compared with experimental data. Based on the spectral analysis of rainfall rate a method for converting rain rate duration statistics to link fade duration statistics is also proposed. Fade duration statistics are presented for terrestrial and satellite links and compared with available experimental data. The agreement between the predicted results and the experimental data has been found to be quite encouraging. Finally, numerical results are presented for various climatic zones, elevation angles and frequencies. Some very useful conclusions concerning the dynamic properties of rain attenuation for a microwave path are deduced. Copyright © 2003 John Wiley & Sons, Ltd.

The paper addresses the problem of the reconstruction of the rainfall field using weather radar observables. It is well known that at the C band and especially at the X band the reconstruction of the rainfall rate profile along the range using absolute (ZH) and differential (ZDR) reflectivity measurements is significantly affected by the attenuation coefficients (i.e. αHand αD. This problem has been long and extensively studied and iterative attenuation correction techniques based on a cumulative procedure were developed, in which the attenuation at nthcell is estimated using the attenuation corrected reflectivity values at previous cell. Usually the attenuation coefficients αH, αDare estimated using non linear parametrizations with (ZH, ZDR), or, if phase measurements are available, using linear parametrizations with the specific differential phase shift KDp. In this work novel approaches based on neural networks (N.N.) have been used.

AbstractConventional estimator of rainfall rate using reflectivity factor and differential reflectivity ZDRbecomes unstable when the measured values of ZDRare small due to measurement errors. An alternate estimator of rainfall rate using reflectivity factor and ZDRis derived, so that this estimator is fairly robust over the full dynamic range of reflectivity factor and ZDR. Simulations are used to study the error structure of this robust estimator in comparison with the conventional estimator of rainfall rate. It is shown that the alternate estimator performs better than the conventional estimator of rainfall rate at all rainfall values. In particular the largest improvement of this estimator is proved to be in light rain. The robust estimator is obtained as a direct regression of rainfall rate against reflectivity factor and ZDRinstead of solving for the drop size distribution.

High rainfall rates have a major impact on agriculture through runoff, erosion, and surface crusting and on other industries through sedimentation, failure of dams, and attenuation of radio waves. Traditional techniques used to measure and record rainfall have produced event totals or hourly rates. These are unsuitable for studying short‐term impacts in brief storms (e.g., 27 mm in 30 min). The objective of this work was to develop an analytical procedure to provide millimeter per minute resolution in rainfall rate from short‐term, tipping‐bucket, rain gauge data. Counts of tips in 1‐min intervals were assembled into an array of accumulated rainfall over time during an event. A cubic spline was fitted to the accumulated rainfall curve and then differentiated to yield the rate curve. Four synthetic rainfall patterns were used to test the technique by matching original and reconstructed curves. Regression of reconstructed rates on input rates resulted in 9 ranging from 0.989 to 0.996. When applied to field data from 8 events (total: 209.5 mm) in July 1984 and 6 events (total: 139 mm) in July 1985, the technique described rainfall rate as a smooth, continuous function of time. This characteristic improves the suitability of the data for input to models of infiltration and runoff that adapt time steps to overcome numerical instability under rapidly changing conditions.

Convective rainfall rate distribution is not very uniform even at a local scale. In order to make an evaluation of it in the urban area of Barcelona, a dense rain gauge network has been stablished in this city. The gauges are of high resolution tilting bucket type. In this paper, preliminary results are presented. The distributions obtained show a strong variation of the rainfall rate for the several parts of the city. The intensity-duration curves for severe storms have been made in the various points of measure together with the areal reduction factors of rainfall. The analysis of raincell movement gives E-W as a predominant direction and a speed in the 3–12 m/s range.

In evaluating the environmental limitations on microwave signal transmission, it was necessary to determine the occurrence of rainfall rates for a number of regions in different parts of the world. Clock-hourly precipitation data were used where available. Where these data were not available, a known empirical technique was employed which correlates clock-hourly rainfall at individual rates to a climatological index. This index uses parameters which are almost universally obtainable—namely, mean annual rainfall and number of days with measurable rain. In the course of this work, the authors found a more general relation between clock-hourly rainfall rate frequencies and the climatological index. Using this relation, rainfall frequencies at any rate are obtained directly from a linear equation and two nomograms. It is also possible to synthesize the complete frequency distribution of rainfall rates and obtain, as a check, the mean annual rainfall. Examples, supplemented by illustrations, are given in the development of this technique and its utilization.

The rainfall rate is a crucial variable in modeling runoff and water erosion. Rainfall rates present a very large spatial and temporal variability. Radars are used to estimate the spatial and temporal changes in rainfall rates during storms. The radar reflectivity is strongly affected by the drop size distribution (DSD) that results from the relative intensity of the microphysical processes involved in rainfall formation. One of the mathematical models applied to quantify the DSD is the Weibull function. Rainfall rate and kinetic energy, and radar reflectivity, are all related to different moments of the DSD. An analytical expression for these moments is proposed in the case of the Weibull distribution. The resulting relationships between radar reflectivity, rainfall rate, and kinetic energy are presented. The use of a scaled DSD that is independent of rainfall rate and characterizes a specific location allows an a priori estimate of relationships between radar reflectivity, rainfall rate, and kinetic energy that will be also characteristics of that location. Radar reflectivity can be used to estimate the kinetic energy of rainfall events. This extends the application of radar to agriculture and environmental purposes such as potential rainfall damages estimates in terms of soil loss, erosion, and sediment transport. An analytical expression of the moments of the drop size distribution corresponding to R and Z are presented for the Weibull distributionThe use of a scaled DSD characterizing a specific location allows to estimate of relationships between Z, R, and dE/dt for that locationZ can be used to estimate dE/dt; the Z (dE/dt) relationship is of a power type, but could be approximated by a linear one

A rapidly switched dual-polarization radar technique is used for the first time to obtain two-dimensional spatial distributions of the statistical characteristics of the sizes and concentration of rain drops in rain. These have been obtained using a high-resolution 10-cm wavelength radar, and the data have been used to estimate rainfall rates within small volumes to a much greater accuracy than is available from conventional radar measurements. The technique also gives a clear distinction between ice particles and rain drops.

A research program involving the study of the attenuation and backscattering by rain of millimeter wavelength radiation has resulted in the requirement for very accurate measurements of rainfall characteristics along paths ranging from 1000 to 2000 ft. A special continuous-wire raingage has been developed which gives an essentially continuous rainfall response by measuring the water level in a cylindrical gage. An accuracy of 0.001 in of rain is obtainable. The responses from two of these gages are compared with those from a number of modified tipping bucket and other gages for several rains in the Austin area.

Designing line of sight (LOS) or satellite link systems, needs yearly point rain rate statistics for several percentages of time, in the locations of interest. This is necessary to estimate within an average year, the time percentage during which attenuation due to rain is significant, and consequently the future link performance and availability. The Letter describes a new procedure which is a probability law for predicting point rainfall rate cumulative distribution. Comparisons between theoretical predictions and experimental data collected in several temperate and tropical localities show good agreement.

An experiment is described in which a rapid-response (10 s) raingauge was operated for 3 years with an 11.6 GHz radiometer directed along an inclined radio path such as might be used with a communications satellite. Cumulative statistics of the path attenuation due to rain are computed from the radiometer results and compared with cumulative rainfall-rate statistics for each of the 3-year periods. The spread between successive years is considered to be small enough for reliable predictions of attenuation to be made from the raingauge results. The application to data obtained from slower-response raingauges is considered.

The correlations of the extent of the melting region with the reflectivity Z measured immediately below the melting layer and the rain rate R near the ground in stratiform and convective precipitation are reported. A total number of 2169 range-height indicator scans recorded by the S-band Chilbolton advanced meteorological radar (CAMRa) have been used for this study.

A comparative statistical study of durations D of rainfall rates R is presented for the temperate region of Barcelona and the tropical region of Ile-Ife (Nigeria). It is shown that the conditional distribution P(D | R) is log normal for the two regions but the mean duration is smaller for the tropical region than for the temperate one. Consideration is given to the value of D¯ for both regions, to the variability σD and to the database required for the statistical study.

Rainfall rate forecasting plays an important role in various human activities. Rainfall forecasting is a challenging task due to the uncertainty of natural phenomena. In this paper, two neural network models are proposed for monthly rainfall rate forecasting. The performance of the proposed model is assesses based on monthly rainfall rate in Ampel, Boyolali, from 2001-2013. The experiment results show that the accuracy of the first model is much better than the accuracy of the second model. Its average accuracy is just above 98%, while the accuracy of the second model is approximately 75%. In additional, both models tend to perform better when the fluctuation of rainfall is low.

An empirical but theoretically sound law derived from rainfall measurements obtained in the Congo (tropical and equatorial climates), and which provides a good fit of rainfall rate distributions observed in various hydrometeorological zones of the world, is presented. This rainfall rate distribution model is compared with the gamma and log-normal ones.

In a previous paper we suggested a three-parameter model providing a good fit for rate distribution. Moreover, this model approximates a log-normal distribution at low rates and a gamma distribution at high rates. In the letter we reduced the number of parameters to two by setting the third one constant. This modified model appears to give good agreement with the experimental data gathered in various locations around the world.

Ruskin Introduces EME5625MD 5-inch Vertical Stationary Louver that Withstands Wind Speeds of 110 mph and Rainfall Rate of 8.8-inches per Hour - Feb 06, 2019 - Ruskin Company [ABSTRACT FROM AUTHOR]

Results from an experiment in the Gulf of Mexico to characterize the underwater sound generated by heavy convective precipitation are reported. For a given rainfall rate, the sound levels are higher at the beginning of the storm or when the rainfall rate is increasing than at the end of the storm or when rainfall rates are decreasing. This is likely to be due to changing storm characteristics during the lifetime of the storm. Very heavy rainfall (rate ≳150 mm/h) generates near surface bubble layers or bubble clouds which attenuate sound energy at higher frequencies (≳15 kHz). The distinctive 15‐kHz peak in the sound spectrum for light rain is absent during heavy rain suggesting that the sound production mechanism previously identified for small drops (0.8‐ to 1.1‐ mm diameter) is suppressed by heavy rain even though small drops are undoubtedly present during heavy rainfall. These data show a very high correlation between underwater sound levels and rainfall rate. Algorithms for measuring rainfall rate from underwater sound will be presented and discussed.

Ruskin Introduces EME3625DFLMD 3-inch Vertical Stationary Louver that Withstands Wind Speed of 110 mph and Rainfall Rate of 8.8 inches Per Hour - Jan 24, 2019 - Ruskin Company [ABSTRACT FROM AUTHOR]

The article emphasizes the importance of proper water dam heights for emergency roof drains, particularly in siphonic drainage systems. Siphonic drains, due to their efficiency and lack of air in the pipework, require careful consideration of water dam heights to prevent overflow engagement, enhance drainage performance and contribute to safer buildings.

Satellite sensors have been widely used for precipitation retrieval, and a number of precipitation retrieval algorithms have been developed using observations from various satellite sensors. The current operational rainfall rate quantitative precipitation estimate (RRQPE) product from the geostationary operational environmental satellite (GOES) offers full disk rainfall rate estimates based on the observations from the advanced baseline imager (ABI) aboard the GOES-R series. However, accurate precipitation retrieval using satellite sensors is still challenging due to the limitations on spatiotemporal sampling of the satellite sensors and/or the uncertainty associated with the applied parametric retrieval algorithms. In this article, we propose a deep learning framework for precipitation retrieval using the combined observations from the ABI and geostationary lightning mapper (GLM) on the GOES-R series to improve the current operational RRQPE product. In particular, the proposed deep learning framework is composed of two deep convolutional neural networks (CNNs) that are designed for precipitation detection and quantification. The cloud-top brightness temperature (BT) from multiple ABI channels and the lightning flash rate from the GLM measurement are used as inputs to the deep learning framework. To train the designed CNNs, the precipitation product multiradar multisensor (MRMS) system from the National Oceanic and Atmospheric Administration (NOAA) is used as target labels to optimize the network parameters. The experimental results show that the precipitation retrieval performance of the proposed framework is superior to the currently operational GOES RRQPE product in the selected study domain, and the performance is dramatically enhanced after incorporating the lightning data into the deep learning model. Using the independent MRMS product as a reference, the deep learning model can reduce the retrieval uncertainty in the operational RRQPE product by at least 31% in terms of the mean-squared error (MSE) and normalized mean absolute error (MAE), and the improvement is more significant in moderate to heavy rain regions. Therefore, the proposed deep learning framework can potentially serve as an alternative approach for GOES precipitation retrievals.

Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning modelsThe best model successfully detects rainfall in 88% of the cases, with R2> 0.87 for hourly rainfall rates on the training siteModel validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2of 0.69 and 0.93 for hourly rainfall rate Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning models The best model successfully detects rainfall in 88% of the cases, with R2> 0.87 for hourly rainfall rates on the training site Model validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2of 0.69 and 0.93 for hourly rainfall rate

Mêdog and Nagqu are two typical regions of the Tibetan Plateau with different geographical locations and climate regimes. These differences may lead to discrepancies in the raindrop size distributions (DSDs) and precipitation microphysical processes between the two regions. This paper investigates discrepancies in the DSDs using disdrometer data obtained during the rainy season in Mêdog and Nagqu. The DSD characteristics are studied under five different rainfall rate categories and two precipitation types (stratiform and convective). For the total datasets, the number concentrations of drops with diameters D> 0.6 (D< 0.6) mm are higher (lower) in Nagqu than in Mêdog. The fitted normalized gamma distributions of the averaged DSDs for the five rainfall rate categories show that Nagqu has a larger (lower) mass-weighted mean diameter Dm(normalized intercept parameter, lgNw) than does Mêdog. The difference in Dmbetween Nagqu and Mêdog increases with the rainfall rate. Convective clusters in Nagqu could be identified as continental-like, while convective precipitation in Mêdog could be classified as maritime-like. The relationships between the shape factor μand slope parameter Λof the gamma distribution model, the radar reflectivity Z, and the rainfall rate Rare also derived. Furthermore, the possible causative mechanism for the notable DSD variation between the two regions during the rainy season is illustrated using reanalysis data and automated weather station observations. Cold rain processes are mainly responsible for the lower concentrations of larger drops observed in Nagqu, whereas warm rain prevails in Mêdog, producing abundant small drops.

Generating a mathematical relationship between rainfall and runoff plays an important role in the decision-making process and control of surface flows. This structure has levels of uncertainty based on the hydrological conditions, land cover, time, depth, and rate of the rainfall events. The main purpose of this study was to determine the degree of uncertainty and its role in calculating runoff generated by rainfall. Therefore, a probabilistic decision model based on copula multivariate functions was developed to predict the depth and maximum rate of the rainfall at different return periods. The relationship between rainfall rate and depth with peak hydrograph flow and runoff volume for flood events over a 37-year period was formulated through fuzzy set theory. The feasible domain of the fuzzy problem was searched using a multi-objective optimization genetic algorithm based on the non-dominated sorting to find the extreme points. The obtained solutions were used as a fuzzy response to calculate the runoff of the Barz plain in Khuzestan province in southwestern Iran. The results showed that the developed fuzzy-probabilistic model was able to predict more than 92% of flood events within the defined fuzzy range. Furthermore, the maximum model error occurred in predicting rainfall depth and runoff volume, and the maximum rainfall rate and runoff flow could be calculated more accurately.

Deep neural network (DNN), equipped with powerful distinctive generalization ability, is one of the most popular retrieval tools for processing ocean remote sensing data from spaceborne microwave radiometers. Considering the signal received by microwave radiometers at different frequencies changes with atmospheric effects, such as rainfall and water vapor in both horizontal (H) and vertical (V) polarization (pol.), and differential signals between H and V pol, these physical factors cannot be ignored in high wind speed retrieval. This study focuses on the integration of physically driven statistical functions with a residual neural network (ResNet) to derive a specialized type of DNN framework for high wind speed retrieval. Specifically, a dynamic contribution-matrix ResNet (DCM-ResNet) model is proposed, which dynamically adjusts the contribution (C) matrix coefficients within the model. These coefficients, reflecting the physical information of radiation transfer, are used to automatically regulate the model’s estimation of high wind speeds. Experiments are conducted using brightness temperatures (TBs) from the Advanced Microwave Scanning Radiometer-2 and wind speeds from the Stepped-Frequency Microwave Radiometer. The experimental results show that the maximum wind speed can reach up to 60 m/s with a root mean square error (RMSE) of 2.92 m/s. In addition, the wind speed RMSE remains stable within 6 m/s as the rainfall rate increases. The contributions of different frequency bands to the C-matrix results are closely related to radiative transfer.

A near-real-time precipitation estimation product derived from geosynchronous Earth-orbiting (GEO) satellite data is highly desirable due to its ability to provide extensive coverage with high spatial and temporal resolution. This research presents a novel Deep-Learning-based Precipitation Estimation algorithm using a Multi-SpatioTemporal network (DLPE-MST), to investigate the potential of Geostationary Operational Environmental Satellite-16 (GOES-16) multitemporal images in precipitation estimation. First, a series of Advanced Baseline Imager (ABI) bispectral satellite images (6.19 and $10.35~\mu \text {m}$ ) from GOES-16 are used as inputs. Second, a module based on 3-D convolutional neural networks (3-D CNNs) is proposed to be embedded into the DLPE-MST for extracting motion features within rainfall areas. Third, a novel loss function, separated domain error (SDE), is proposed for DLPE-MST to mitigate the issue of underestimation arising from imbalanced precipitation datasets. Finally, to assess the feasibility of the DLPE-MST, GOES-16 satellite images covering the eastern Continental United States (CONUS) of America during the summer of 2020–2021 are utilized to generate raster maps depicting hourly rainfall rates at a resolution of $ 0.04^{\circ } \times 0.04^{\circ }$ . The experimental results indicate that our algorithm outperforms others in terms of probability of detection (POD) and correlation coefficient (CC), achieving scores of 91.79% and 0.58, respectively. The statistical analysis of multiple rainfall events also demonstrates that the DLPE-MST outputs are closer to the ground truth compared to other products. Furthermore, the SDE shows significant potential in alleviating the underestimation of heavy rain events. After testing, this algorithm takes only 0.09 s to generate one raster map of the rainfall rate.

Several hydrologists recently stated that a large portion of the world’s population will face “water stress” in the coming years due to a variety of factors, including global mismanagement of fresh water near coastal plains, which is still being ignored and has become polluted. The alluvial plain of Wadi El Nil is one of these coastal plains, located in one of the wettest regions of Algeria with an annual rainfall rate of around 1000 mm. To quantify and qualify the water potential and to design a water management policy, a study was conducted using a complex of tools, such as geological, geophysical by electrical methods, hydro-climatic, and hydrogeological, to determine the lithology, geometry, and hydrodynamic characteristics of the aquifer.

Physical characteristics of precipitation, like temporal and spatial variability, jointly with coverage and costs of conventional meteorological devices for quantitative rainfall estimation (i.e., rain gauges, disdrometers, weather radars) make the precipitation monitoring a complex task. However, real-time rainfall maps are an important tool for many applications, dealing with environment, social activities, and business. Recently, the use of “opportunistic” methods to estimate rainfall has been investigated, highlighting the possibility to exploit inexpensive opportunities to augment information about precipitation. This article deals with smart low-noise blocks (SmartLNBs) converters, which are commercially available interactive digital video broadcasting (DVB) receivers designed to be used as bidirectional modems for commercial interactive TV applications. In the last few years, an algorithm that converts the SmartLNB raw data into attenuation values, from which the rainfall rate is obtained, has been developed and evaluated. The aim of this article is to describe the improvements of the rainfall estimation from SmartLNBs brought by significant changes in the data acquisition from SmartLNB and by algorithms’ update. One year of data collected in Rome and Tuscany (Italy) are analyzed to test the performance of SmartLNB in estimating rainfall accumulation with respect to co-located rain gauges and disdrometer in the new configuration. Comparing SmartLNB and disdrometer data in Rome, we obtained root mean square error (RMSE) equal to 7.3 mm, normalized mean absolute error (NMAE) equal to 51%, with a correlation coefficient of 0.67, that can point out the maturity of the technique.

Nitrate (NO-3) and nitrite (NO-2) leaching threatens groundwater quality. Soil C:N ratio, i.e., the ratio of soil organic carbon to total nitrogen, affects mineralization, nitrification, and denitrification; however, its mechanism for driving soil NO-3and NO-2accumulation and leaching remains unclear. Here, a field investigation in a fluvo-aquic soil and a soil column experiment were performed to explore the relationships between soil C:N ratio and soil NO-3and NO-2leaching in three soil layers (0–20, 20–40, and 40–60 cm) under heavy rainfall (rainfall rate > 25 mm d–1). The field investigation results showed that both soil NO-3-N and NO-2-N contents decreased exponentially (P <0.001) with increasing soil C:N ratio in each soil layer. Furthermore, negative exponential relationships (P< 0.001) were found between soil C:N ratio and both NO-3-N and NO-2-N concentrations in soil solution in each soil layer under heavy rainfall. The soil column divided into three layers was leached with simulated heavy rainfall; the results confirmed negative exponential relationships (P< 0.05) between soil C:N ratio and both NO-3-N and NO-2-N concentrations in the leachate from each soil layer. A total of 18 soil samples obtained from three depths at six field sites during the rainy season were used to elucidate the microbial mechanisms induced by soil C:N ratio using high-throughput sequencing and real-time polymerase chain reaction. High abundances of ammonifying bacteria (Flavobacterium, Bacillu,and Pseudomonas), ammonia-oxidizing bacteria (Nitrosospira), and nirS/Kgene were observed when soil C:N was low, concomitant with low abundances of NO-2-oxidizing bacteria (Nitrospira) and narGgene. Partial least squares path modeling showed that the high NO-3and NO-2levels at low soil C:N ratio might be attributed to the inhibition of NO-3reduction (i.e., low narGgene) and NO-2oxidation (i.e., low Nitrospira) and thus the accumulation of soil NO-3and NO-2, respectively. Therefore, the leaching of NO-2and NO-3in low C:N soils requires more attention during the rainy season.

Shutterstock A rapid rain burst (highlighted rain pattern) close to Sydneydetected in Wollongong radar. The chart below shows the increases in the rate of rainfall ofrapid rain bursts for each of three weather radars (at Newcastle,Terrey Hills and Wollongong). It's calculated by dividing the rainfall rate of thetop 5% of rapid rain bursts each year by the average rate for suchrain bursts over the whole period covered by that radar. [Extracted from the article]

Trend analysis is frequently utilised to identify the changes in meteorological and hydrologic time series data, such as rainfall and temperature. The variations in the intensity, rainfall pattern and temperature have gradually changed globally. Hence, in this study, an attempt was made to analyse the decadal rainfall and surface air temperature data to understand the microclimatic variations in the Miri coastal region of NW Borneo. A data series of records for daily total rainfall amount and daily surface temperature of 11 years from 2010 to 2021 was studied and analysed. In addition, representative rainwater and groundwater samples were collected and analysed for hydrochemical parameters and oxygen and hydrogen isotopes. A detailed literature review was carried out on rainfall patterns in Malaysia, which was used for the comparative study. Interpretation of results shows that the northeast monsoon (NEM) contributed a higher total rainfall rate with lower daily mean surface air temperature over the years compared to the southwest monsoon (SWM). The recorded data for rainfall amounts in SWM for the month of May, July, August and September were higher, particularly for the years 2010 and 2020. During NEM, a higher rainfall amount was recorded in the month of January for several years. February month has always been among the driest month in NEM, and September has been the wettest month throughout the year during SWM. The isotopic values of rainwater indicate a similar moisture source to the regional precipitation trend. Groundwater isotopes reveal the low water-rock ratio of retrograde exchange between water and primary silicate minerals in the aquifer. The moisture source of the precipitation was contributed from both oceanic and continent, affecting the rainfall intensity in this region. This study is a crucial outcome to determine the potential impacts of microclimatic variations on the rainfall patterns in the Miri coastal region.

In this article, a robust process to detect the existence of in-excess attenuation values in the next future time-steps based on measured data from previous time-steps is provided for satellite channel measurements campaign at frequencies above 10 GHz. The straightforward process of anomaly detection is applied, where “normal” data, i.e., attenuation values lower than a threshold, are used to learn their predictive error probability. Afterward, a sequence is detected as “normal” or “abnormal” based on the Mahalanobis distance of its errors from the learned distribution. deep-learning architectures such as encoder-decoder and traditional paradigms are investigated and tested for different regions (Greece and U.K.) and frequency bands (Q and Ka downlinks). The method’s performance is very promising and the rainfall rate information seems valuable and adequate for attenuation’s detection. Finally, under a “plug-and-play” logic, the trained detectors in Greece exhibit high performance also in U.K. data showing the robustness of the proposed detection ability.

The Gated Recurrent Unit (GRU) neural network has great potential in estimating and predicting a variable. In addition to radar reflectivity (Z), radar echo-top height (ET) is also a good indicator of rainfall rate (R). In this study, we propose a new method, GRU_Z-ET, by introducing Z and ET as two independent variables into the GRU neural network to conduct the quantitative single-polarization radar precipitation estimation. The performance of GRU_Z-ET is compared with that of the other three methods in three heavy rainfall cases in China during 2018, namely, the traditional Z-Rrelationship (Z=300R1.4), the optimal Z-Rrelationship (Z=79R1.68) and the GRU neural network with only Zas the independent input variable (GRU_Z). The results indicate that the GRU_Z-ET performs the best, while the traditional Z-Rrelationship performs the worst. The performances of the rest two methods are similar.

This article presents two new state-of-the-art spatial rain field interpolation convolutional neural networks (SRFICNNs), referred to as learned deviation (LD) and learned interpolation (LI) models, for predicting the point rain rate at finer spatial scales. The main contribution is the successful introduction of the prior-art deep learning technique into high-resolution (HR) rainfall rate prediction with significant improvement in accuracy. This is very important for the effective implementation of fade mitigation techniques for both terrestrial and satellite networks. The comparison of the models’ performances with ground truth (radar measurements) shows that the proposed models give an excellent mean square error (MSE) and structural SIMilarity (SSIM) in rainfall field reconstruction if the network depth falls in the range of 15–25 weight layers. The final model uses 20 layers for HR point rain rate prediction. Further study shows that the LD model offers a faster convergence and yields a more accurate rain rate prediction. In particular, this article compares the rain rate exceedance distribution and Log-Normality property from the model estimates with values calculated from measured data. Results show that the LD model gives a highly accurate estimate of these two indices with the corresponding root mean square (rms) error of $5.1709\times 10^{-4}$ and 0.0013, respectively.

Abstract: The collection mechanism inside the cloud is associated with the coalescence process. It controls the rainfall rate in the presence of high moisture content and low cloud condensation nuclei concentrations. The effectiveness of the coalescence mechanism is determined by the magnitude of the coalescence efficiency. This study compared the effectiveness of six coalescence efficiencies to identify the appropriate formulation, which can simulate the cloudburst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 IST (06:15 UTC). The coalescence efficiencies have been estimated between the lifting condensation levels and freezing levels over 100 equidistance intervals, with variation in the bins of colliding droplet pairs. The comparative analysis shows that Ochs’s formulation estimated high values of coalescence efficiencies ranging between 60 and 100% for all bins of droplet diameters during the cloudburst event. The maximum values of coalescence efficiency were found to be high for small cloud droplet diameter of 1–100 µm for collector droplet diameter of 50–7000 µm. The estimates provided by Seifert and Chen and Liu were below 80%, while Low and List, Beard and Ochs estimated coalescence efficiency values of up to 70% for all-collector cloud droplet diameters and small cloud droplet diameters. However, the estimated coalescence efficiencies by Brazier–Smith formulation are very low, not exceeding 20%, for all collector cloud droplet diameters and small cloud droplet diameters. The selection of appropriate coalescence parametrization is essential for appropriately simulating rainfall intensities using microphysics parametrization during extreme weather events. Research highlights:

The coalescence process plays an important role in determining rain rate, and modelling it is a challenge.

Six coalescence efficiencies have been estimated for the cloud burst event of Sauni Binsar, Uttarakhand, India.

Ochs’s formulation estimated high values of coalescence efficiencies ranging between 60 and 100% for all bins of droplet diameter, which was expected.

Seifert and Chen and Liu formulations estimated coalescence efficiencies below 80%, and Low and List and Beard and Ochs estimated coalescence efficiency up to 70% for the all-collector cloud droplet diameter and small cloud droplet diameter.

The estimated coalescence efficiencies by Brazier–Smith formulation are very low, not exceeding 20%.

Abstract: Cloudburst events are characterized by very high rainfall rates (100 mm h−1) over a small area (~10×10 km). It catastrophically damages the properties and causes loss of lives. Therefore, it is essential to understand the nature of the microphysical processes that contributed to producing such a high rain rate. Collision processes inside the cloud are vital in determining the rainfall rate. Hence, in the present study, we have evaluated a comparative performance of disparate collision efficiencies based on different formulations during the cloud burst event of 10 June 2021 over Sauni Binsar, Uttarakhand, at 11:45 AM IST (06:15 UTC 10 June 2021). The lifting condensation level (LCL), lifting deposition level (LDL), and lifting freezing levels (LFL) have been calculated using the ERA5 dataset. The collision efficiencies were determined on 100 levels between the lifting condensation and freezing levels. The appraisal of eight formulations shows that Onishi, Beard and Grover, Bohm, and Jin formulations yielded higher collision efficiencies during the cloudburst event, which is expected. Meanwhile, the estimated collision efficiency based on Ahmad’s formulation is high for specific bins only. The formulations by Barnet, Long, and Lee and Baik, however, yielded very low values of collision efficiencies during the cloud burst event. Research highlights:

Collision processes inside the cloud are vital in determining the rainfall rates especially during cloud burst.

Eight collision efficiency formulations have been apprised during the cloud burst event of Sauni Binsar, Uttarakhand, India.

The complex interaction between synoptic systems and orography caused atmosphere over Sauni Binsar to be convective and moist few hours before the cloud burst event.

Collision efficiency formulation by Onishi, Beard and Grover, Bohm, and Jin estimated higher collision efficiencies, which is appropriate, while Ahmad estimated high values for specific bins only.

Collision efficiency formulation by Barnet, Long, and Lee and Baik estimated very low values.

The rational method is commonly used to estimate the design floods in catchments. An accurate estimation of surface runoff and the related design floods depends on the runoff coefficient precision, which is associated with several factors such as rainfall and soil infiltration rate. In the rational method, the design runoff coefficient (CT) is defined as a function of land use, soil type, slope, and return period. A technique is proposed here to compute CTbased on the regional analysis of daily rainfall and soil conservation service curve number (SCS-CN) infiltration parameters. Daily rainfall data of 83 rain gauge stations in Sothern Iran (Fars province) were used to calculate the CTfor various land uses and return periods for four rainfall rate categories. Equations were introduced to determine CTas a function of the return period and curve number in different catchments of the world. The regression correlation coefficient was calculated to be above 0.99 for the suggested equations. Based on the suggested method, for design purpose, the CN standard tables in the SCS method were converted into CTtables for the return period 10–500 years in Fars province, Iran.

Strong convective systems and the associated heavy rainfall events can trig-ger floods and landslides with severe detrimental consequences. These events have a high spatio-temporal variability, being difficult to predict by standard meteorological numerical models. This work proposes the M5Images method for performing the very short-term prediction (nowcasting) of heavy convective rainfall using weather radar data by means of a convolutional recurrent neural network. The recurrent part of it is a Long Short-Term Memory (LSTM) neural network. Prediction tests were performed for the city and surroundings of Campinas, located in the Southeastern Brazil. The convolutional recurrent neural network was trained using time series of rainfall rate images derived from weather radar data for a selected set of heavy rainfall events. The attained pre-diction performance was better than that given by the persistence forecasting method for different prediction times.

AbstractWe introduce an idealised model for overland flow generated by rain falling on a hillslope. Our prime motivation is to show how the coalescence of runoff streams promotes the total generation of runoff. We show that, for our model, as the rate of rainfall increases in relation to the soil infiltration rate there is a distinct phase change. For low rainfall (the subcritical case) only the bottom of the hillslope contributes to the total overland runoff, while for high rainfall (the supercritical case) the whole slope contributes and the total runoff increases dramatically. We identify the critical point at which the phase change occurs, and show how it depends on the degree of coalescence. When there is no stream coalescence the critical point occurs when the rainfall rate equals the average infiltration rate, but when we allow coalescence the critical point occurs when the rainfall rate is less than the average infiltration rate, and increasing the amount of coalescence increases the total expected runoff.

Drainage networks emerge due to the movement of sediment driven by climatic and tectonic forcings. Previous observations revealed the dependence of drainage density (Dd) on climatic factors such as mean annual precipitation (MAP). Specifically, it has been observed in intermediate climate (~175 mm < MAP < 700 mm) that Dddecreases with increasing MAP. This declining trend has been argued to be attributed to biotic activity, that is, the dominance of vegetation growth over runoff erosive force. Using high‐resolution topographic data from a physical experiment, we show that increasing rainfall may result in decreasing Ddeven in the absence of biotic activity. Increasing rainfall rate results in narrower and less variable bifurcation angle and relatively longer links. The response of the evolving network in terms of bifurcation angle continues beyond the time when Ddhas reached a new stable value, indicating that the network's topological response is relatively slower than the geomorphic response. Drainage density decreases with increasing rainfall rate in a physical experiment without vegetationConvergent and divergent features show distinct responses to the change in rainfallHigher rainfall rate results in narrower and less variable bifurcating angles and longer links

The size of a tropical cyclone (TC), measured by the area of either rainfall or wind, is an important indicator for the potential damage by TC. Modeling studies suggested that aerosols tend to enhance rainfall in the outer rainbands, which enlarges the eyewall radius and expands the extent of rainfall area. However, no observational evidence has yet been reported. Using TC rainfall area and aerosol optical depth (AOD) data, we find that aerosols have a distinguishable footprint in the TC size. Other dynamical factors for TC size, such as relative SST and Coriolis parameter, are also quantified and discussed. We show that, on average, TC rainfall size increases 9–20 km for each 0.1 increase of AOD in the western North Pacific. This finding implies that anthropogenic aerosol pollution can increase not only TC rainfall rate, but also TC rainfall area, resulting in potentially more destructive flooding affecting larger areas. The size of a tropical cyclone (TC), measured by the area of either rainfall or wind, is an important indicator for the potential damage by TC. No observational evidence about the impact of aerosols on the TC size has yet been reported. Using TC rainfall area and aerosol optical depth (AOD) data, we find that aerosols have a distinguishable footprint in the TC size. On average, TC rainfall size increases by 9–20 km for each 0.1 increase in AOD in the western North Pacific. This finding implies that anthropogenic aerosol pollution can increase not only the TC rainfall rate as found in previous studies, but also the TC rainfall area, resulting in potentially more intensive flooding affecting larger areas. It is worthy to note that factors other than the aerosols, such as relative sea surface temperature, could also contribute to the changes of TC rainfall area. Aerosols enlarge the tropical cyclone rainfall area in the western North Pacific, which is about 9–20 km for each 0.1 increase in AODThe location of the maximum TC rainfall rate, i.e., TC eyewall, moves farther away from the TC center as AOD increasesTotal rainfall amount of TC is also found to increase with AOD

Free space optical communication (FSO) system has remarkably gained importance of late due to the various advantages associated with it. However, before deployment of FSO communication system, an understanding of the laser beam scintillation due to the atmospheric as well as other weather-induced losses need to be understood. This paper mainly focuses on simulative performance analysis of an FSO system under rain conditions using Optisystems commercial software and rainfall data. Thus, the available models which are the Suriza, Carbonnea, Japan and Samir models were used to determine the rainfall-induced attenuation for a given rainfall rate. Unlike in the previous articles that only used on off keying as the modulation scheme at the transmitter, we have used modified duobinary return to zero (MDRZ) modulation. Further, to improve the MDRZ-based FSO performance in rain conditions, we have used the single-input multiple-output (SIMO) concept with equal gain combining (EGC) at the receiver and again proved that the improvement in BER, Qfactor and received optical power suggests that MDRZ-based SIMO FSO communication results in a remarkable improvement.